/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    DiscreteEstimator.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.estimators;

import weka.core.Aggregateable;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Utils;

/**
 * Simple symbolic probability estimator based on symbol counts.
 * 
 * @author Len Trigg (trigg@cs.waikato.ac.nz)
 * @version $Revision$
 */
public class DiscreteEstimator extends Estimator implements IncrementalEstimator, Aggregateable<DiscreteEstimator> {

    /** for serialization */
    private static final long serialVersionUID = -5526486742612434779L;

    /** Hold the counts */
    private final double[] m_Counts;

    /** Hold the sum of counts */
    private double m_SumOfCounts;

    /** Initialization for counts */
    private double m_FPrior;

    /**
     * Constructor
     * 
     * @param numSymbols the number of possible symbols (remember to include 0)
     * @param laplace    if true, counts will be initialised to 1
     */
    public DiscreteEstimator(int numSymbols, boolean laplace) {

        m_Counts = new double[numSymbols];
        m_SumOfCounts = 0;
        if (laplace) {
            m_FPrior = 1;
            for (int i = 0; i < numSymbols; i++) {
                m_Counts[i] = 1;
            }
            m_SumOfCounts = numSymbols;
        }
    }

    /**
     * Constructor
     * 
     * @param nSymbols the number of possible symbols (remember to include 0)
     * @param fPrior   value with which counts will be initialised
     */
    public DiscreteEstimator(int nSymbols, double fPrior) {

        m_Counts = new double[nSymbols];
        m_FPrior = fPrior;
        for (int iSymbol = 0; iSymbol < nSymbols; iSymbol++) {
            m_Counts[iSymbol] = fPrior;
        }
        m_SumOfCounts = fPrior * nSymbols;
    }

    /**
     * Add a new data value to the current estimator.
     * 
     * @param data   the new data value
     * @param weight the weight assigned to the data value
     */
    @Override
    public void addValue(double data, double weight) {

        m_Counts[(int) data] += weight;
        m_SumOfCounts += weight;
    }

    /**
     * Get a probability estimate for a value
     * 
     * @param data the value to estimate the probability of
     * @return the estimated probability of the supplied value
     */
    @Override
    public double getProbability(double data) {

        if (m_SumOfCounts == 0) {
            return 0;
        }
        return m_Counts[(int) data] / m_SumOfCounts;
    }

    /**
     * Gets the number of symbols this estimator operates with
     * 
     * @return the number of estimator symbols
     */
    public int getNumSymbols() {

        return (m_Counts == null) ? 0 : m_Counts.length;
    }

    /**
     * Get the count for a value
     * 
     * @param data the value to get the count of
     * @return the count of the supplied value
     */
    public double getCount(double data) {

        if (m_SumOfCounts == 0) {
            return 0;
        }
        return m_Counts[(int) data];
    }

    /**
     * Get the sum of all the counts
     * 
     * @return the total sum of counts
     */
    public double getSumOfCounts() {

        return m_SumOfCounts;
    }

    /**
     * Display a representation of this estimator
     */
    @Override
    public String toString() {

        StringBuffer result = new StringBuffer("Discrete Estimator. Counts = ");
        if (m_SumOfCounts > 1) {
            for (int i = 0; i < m_Counts.length; i++) {
                result.append(" ").append(Utils.doubleToString(m_Counts[i], 2));
            }
            result.append("  (Total = ").append(Utils.doubleToString(m_SumOfCounts, 2));
            result.append(")\n");
        } else {
            for (int i = 0; i < m_Counts.length; i++) {
                result.append(" ").append(m_Counts[i]);
            }
            result.append("  (Total = ").append(m_SumOfCounts).append(")\n");
        }
        return result.toString();
    }

    /**
     * Returns default capabilities of the classifier.
     * 
     * @return the capabilities of this classifier
     */
    @Override
    public Capabilities getCapabilities() {
        Capabilities result = super.getCapabilities();
        result.disableAll();

        // class
        if (!m_noClass) {
            result.enable(Capability.NOMINAL_CLASS);
            result.enable(Capability.MISSING_CLASS_VALUES);
        } else {
            result.enable(Capability.NO_CLASS);
        }

        // attributes
        result.enable(Capability.NUMERIC_ATTRIBUTES);
        return result;
    }

    @Override
    public DiscreteEstimator aggregate(DiscreteEstimator toAggregate) throws Exception {

        if (toAggregate.m_Counts.length != m_Counts.length) {
            throw new Exception("DiscreteEstimator to aggregate has a different " + "number of symbols");
        }

        m_SumOfCounts += toAggregate.m_SumOfCounts;
        for (int i = 0; i < m_Counts.length; i++) {
            m_Counts[i] += (toAggregate.m_Counts[i] - toAggregate.m_FPrior);
        }

        m_SumOfCounts -= (toAggregate.m_FPrior * m_Counts.length);

        return this;
    }

    @Override
    public void finalizeAggregation() throws Exception {
        // nothing to do
    }

    protected static void testAggregation() {
        DiscreteEstimator df = new DiscreteEstimator(5, true);
        DiscreteEstimator one = new DiscreteEstimator(5, true);
        DiscreteEstimator two = new DiscreteEstimator(5, true);

        java.util.Random r = new java.util.Random(1);

        for (int i = 0; i < 100; i++) {
            int z = r.nextInt(5);
            df.addValue(z, 1);

            if (i < 50) {
                one.addValue(z, 1);
            } else {
                two.addValue(z, 1);
            }
        }

        try {
            System.out.println("\n\nFull\n");
            System.out.println(df.toString());
            System.out.println("Prob (0): " + df.getProbability(0));

            System.out.println("\nOne\n" + one.toString());
            System.out.println("Prob (0): " + one.getProbability(0));

            System.out.println("\nTwo\n" + two.toString());
            System.out.println("Prob (0): " + two.getProbability(0));

            one = one.aggregate(two);

            System.out.println("\nAggregated\n");
            System.out.println(one.toString());
            System.out.println("Prob (0): " + one.getProbability(0));
        } catch (Exception ex) {
            ex.printStackTrace();
        }
    }

    /**
     * Main method for testing this class.
     * 
     * @param argv should contain a sequence of integers which will be treated as
     *             symbolic.
     */
    public static void main(String[] argv) {

        try {
            if (argv.length == 0) {
                System.out.println("Please specify a set of instances.");
                return;
            }
            int current = Integer.parseInt(argv[0]);
            int max = current;
            for (int i = 1; i < argv.length; i++) {
                current = Integer.parseInt(argv[i]);
                if (current > max) {
                    max = current;
                }
            }
            DiscreteEstimator newEst = new DiscreteEstimator(max + 1, true);
            for (int i = 0; i < argv.length; i++) {
                current = Integer.parseInt(argv[i]);
                System.out.println(newEst);
                System.out.println("Prediction for " + current + " = " + newEst.getProbability(current));
                newEst.addValue(current, 1);
            }

            DiscreteEstimator.testAggregation();
        } catch (Exception e) {
            System.out.println(e.getMessage());
        }
    }
}
